A CT scanner includes an X-ray tube that emits radiation that traverses an examination region and an object therein. A detector array located opposite the examination region across from the X-ray tube detects radiation that traverses the examination region and the object therein and generates projection data indicative of the examination region and the object therein. A reconstructor processes the projection data and reconstructs volumetric image data indicative of the examination region and the object therein.
In spectral or multi-energy CT, multiple attenuation projection data sets are acquired, which represent attenuation properties of a scanned object for different X-ray spectra. The multiple sets can be acquired through kVp switching, dual layer detectors, counting detectors, and/or otherwise. Each attenuation data set is acquired from a different spectral channel of the spectral or multi-energy CT system. Based on these multiple attenuation projection data sets, physical object properties can be determined, which is called material decomposition.
A possible approach for the material decomposition is decomposition in projection domain. For projection domain decomposition, the material decomposition is performed by converting attenuation line integrals of the different spectral channels into material line integrals. Due to a non-linearity of this material conversion, a bias is induced by noise in the attenuation line integrals (so-called noise induced bias). The strength of this bias directly depends on an amount of noise in the attenuation projection data sets. In many cases the bias will be unacceptably high, leading to strong deviations of reconstructed material images from a ground truth, thus preventing a reasonable quantitative evaluation for diagnostic purposes.
Thus, de-noising strategies are implemented to reduce noise in the attenuation line integrals, thus also reducing the bias in the decomposed material line integrals. Nevertheless, since an overly strong de-noising on the attenuation projection data sets leads to a loss of structures in the final images, some noise remains in the de-noised attenuation line integrals. Thus, there is still significant bias in the decomposed data. Furthermore, the remaining noise in the de-noised attenuation projection data still leads to severe noise in the decomposed data, since the decomposition is an ill-posed problem that strongly amplifies noise.
As a summary, the non-linearity of the material decomposition of a spectral or multi-energy CT system leads to noise induced bias, even if the noise on input data is reduced by de-noising. This bias leads to deviations of reconstructed material images from the ground truth, thus producing wrong values in quantitative evaluations for diagnostic purposes.
WO 2014/080311 A1 discloses an approach to reduce in the projection domain correlated noise from spectral/multi-energy projection data. This can be achieved based at least on variances of the material line integrals and a covariance there between.